Method for determining transportation vehicle customer satisfaction

ABSTRACT

Within a method for predicting customer satisfaction for a transportation vehicle there is measured for the transportation vehicle a Noise, Vibration and Harshness (NVH) level within the transportation vehicle when an engine which powers the transportation vehicle is operating at wide open throttle. The method further provides for determining a customer satisfaction value for a specific transportation vehicle by means of interpolation or extrapolation from an existing correlation for a group of transportation vehicles within the same class. Such a correlation is obtained employing a Transformed Gamma Distribution (TGD) model or an aggregate combination of Transformed Gamma Distribution (TGD) models.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to transportation vehicles. Moreparticularly, the present invention relates to methods for determiningtransportation vehicle customer satisfaction.

2. Description of the Related Art

In the process of transportation vehicle design, development andmanufacturing, transportation vehicle manufacturers often pay particularattention to manufacturing and developing transportation vehicledesigns, which provide transportation vehicles, which in turn providehigh levels of customer satisfaction. High levels of customersatisfaction are desirable within the context of transportation vehicledesign, development and manufacturing since high levels of customersatisfaction generally provide with respect to an individualtransportation vehicle manufacturer and an individual customer brandloyalty which provides for purchase of multiple additionaltransportation vehicles from the individual transportation vehiclemanufacturer by the individual customer.

While high levels of customer satisfaction are thus clearly desirablewithin the art of transportation vehicle design, development andmanufacturing, providing transportation vehicle designs which may bedeveloped and manufactured to provide transportation vehicles whichprovide high levels of customer satisfaction is nonetheless not entirelywithout problems within the art of transportation vehicle design,development and manufacturing.

In that regard, it is often difficult to provide transportation vehicledesigns which may be developed and manufactured to provide high levelsof customer satisfaction, in part, since while transportation vehicledesign, development and manufacturing is typically directed towardsphysical components and subassemblies which are employed when designing,developing and manufacturing a transportation vehicle, transportationvehicle customer satisfaction is in contrast generally recognized as asubjective characteristic which is often difficult to directly correlatewith respect to specific transportation vehicle physical components andsubassemblies which are employed when designing, developing andmanufacturing a transportation vehicle.

There thus exists within the art of transportation vehicle design,development and manufacturing a continuing need for methods fordetermining transportation vehicle customer satisfaction, preferably atearly stages in design, development and manufacturing of transportationvehicles.

It is towards the foregoing object that the present invention isdirected.

SUMMARY OF THE INVENTION

In accord with the object towards which the present invention isdirected, there is provided by the present invention a method forpredicting customer satisfaction for a transportation vehicle. In itsmost general sense, the method realizes the foregoing object bymeasuring for the transportation vehicle a Noise, Vibration andHarshness (NVH) level within the transportation vehicle when an enginewhich powers the transportation vehicle is operating at wide openthrottle. Within the present invention and the preferred embodiment ofthe present invention, the Noise, Vibration and Harshness (NVH) level istypically and preferably an interior loudness level.

In a more specific sense, the present invention also provides a methodfor determining for a specific transportation vehicle an as yetundetermined expected customer satisfaction level, which might otherwisebe determined, for example, by survey, where the as yet undeterminedexpected customer satisfaction level is determined by extrapolation orinterpolation from a series of measurements of Noise, Vibration andHarshness (NVH) levels for a group of transportation vehicles, typicallyand preferably of a class analogous or equivalent to the class oftransportation vehicle in which the specific transportation vehicle is amember.

BRIEF DESCRIPTION OF THE DRAWING

The object, features and advantages of the present invention areunderstood within the context of the Description of the PreferredEmbodiment, as set forth below. The Description of the PreferredEmbodiment is understood within the context of the accompanyingdrawings, which form a material part of this disclosure, wherein:

FIG. 1 shows a schematic process flow diagram illustrating a series ofprocess steps in accord with the present invention.

FIG. 2 shows a graph of the Percent Measured High Customer SatisfactionLevel versus Phons Weighted Average derived from Phons versus EngineRevolutions Per Minute in accord with an example of the presentinvention.

FIG. 3 shows a graph of the Predicted Percent High Customer SatisfactionLevel versus Customer Survey Measured Percent High Customer SatisfactionLevel in accord with an example of the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENT

Referring now to FIG. 1, there is shown a schematic process flow diagramillustrating a series of process steps in accord with the method of thepresent invention. In particular, there is illustrated within FIG. 1, inconjunction with reference numeral 10, the first process step in accordwith the method of the present invention. In accord with the processstep which corresponds with reference numeral 10, there is firstprovided within the present invention a group of transportationvehicles.

Within the context of the preferred embodiment of the present invention,the group of transportation vehicles which is provided is typically andpreferably selected from a single class of transportation vehicles,where the single class of transportation vehicles is selected from thegroup including but not limited to sports passenger transportationvehicles, sub-compact passenger transportation vehicles, compactpassenger transportation vehicles, standard passenger transportationvehicles, luxury passenger transportation vehicles, sport utilitytransportation vehicles, light utility transportation vehicles and heavyutility transportation vehicles.

Typically and preferably, within the preferred embodiment of the presentinvention there is provided within the group of transportation vehiclesa number of transportation vehicles ranging from about 10 to about 30.Similarly, typically and preferably, the group of transportationvehicles which is provided from the single class of transportationvehicles will encompass a multiplicity of transportation vehiclemanufacturers which manufactures transportation vehicles within thesingle class of transportation vehicles. Yet similarly, typically andpreferably, each transportation vehicle within the group oftransportation vehicles from the single class of transportation vehicleswill typically and preferably have an odometer mileage of from about3000 to about 7000 miles, as accrued over a usage of from about 2.5 toabout 3.5 months, which usage is representative of normal usage absentextraordinary wear.

Referring again to FIG. 1, there is illustrated in conjunction withreference numeral 20 the next process step in accord with the method ofthe present invention. In accord with the process step which correspondswith reference numeral 20, there is measured for each transportationvehicle within the group of transportation vehicles a subjectivecustomer satisfaction level of a customer who operates thetransportation vehicle, to thus provide a series of subjective customersatisfaction levels for the group of transportation vehicles.

Within the preferred embodiment of the present invention with respect tothe measurement of subjective customer satisfaction levels, themeasurement of subjective customer satisfaction levels may be directedtowards a general subjective customer satisfaction level, or as anadjunct or in the alternative directed more specifically towardsspecific attributes of transportation vehicle customer satisfaction,such as but not limited to transportation vehicle Noise, Vibration andHarshness (NVH) customer satisfaction, which further may be defined interms of transportation vehicle noise and transportation vehiclevibration. Within the preferred embodiment of the present invention withrespect to the measurement of the series of subjective customersatisfaction levels of the group of transportation vehicles, suchsubjective customer satisfaction levels may typically and preferably bedetermined while employing survey methods wherein owners of a targetclass of transportation vehicles registered for a requisite time periodare surveyed regarding their subjective customer satisfaction level withrespect to a transportation vehicle which they have purchased within thetarget class. Typically and preferably, such subjective customersatisfaction level survey data is obtained within a format of a rankingon a scale of 1-10, where 1 represents a minimum amount of customersatisfaction and 10 represents a maximum amount of customersatisfaction. For the purposes of the present invention, a customersatisfaction level rating of either 9 or 10 is understood as a “high”customer satisfaction level rating.

Referring again to FIG. 1, there is illustrated in conjunction withreference numeral 30 the next process step in accord with the method ofthe present invention. In accord with the process step, whichcorresponds with reference numeral 30, there is measured for eachtransportation vehicle within the group of transportation vehicles a NVHlevel to provide a series of NVH levels. Although the NVH for which theNVH level is measured may be selected from the group of NVH parametersincluding but not limited to vibration (such as but not limited tosteering wheel vibration, seat track vibration, stick shift vibrationand frame/chassis vibration) and loudness (such as but not limited tointerior loudness, engine loudness, powertrain loudness, intake systemloudness and exhaust system loudness), where a loudness, such as a noisefield loudness, may be correlated with a sound quality, for thepreferred embodiment of the present invention, it has been determinedexperimentally to be particularly preferable to employ as a NVHparameter a vehicular interior loudness measured at a driver's outboardear within a vehicle interior under conditions of a wide open throttle(i.e., from about 1000 to the maximum revolutions per minute (rpm)) foran engine which powers the transportation vehicle.

Typically and preferably, the interior loudness measurement will beobtained employing methods as are in general conventional in the art ofloudness measurement. For example and without limitation, such interiorloudness measurements may be obtained by applying a Zwicker ⅓ octavediffuse field method to the recorded noise frequency spectrum levels toproduce the Sones loudness levels, as is disclosed, for example and alsowithout limitation, within ISO Standard 532-1975(E). For the preferredembodiment of the present invention, the interior loudness is typicallyand preferably measured in Sones.

Referring again to FIG. 1, there is illustrated in conjunction withreference numeral 40 the next process step in accord with the method ofthe present invention. In accord with the process step which correspondswith reference numeral 40, there is determined from the series of NVHlevels at least one NVH variable and a resulting series of NVH variablelevels.

For the preferred embodiment of the present invention where the NVHlevel measurement is an interior loudness measurement at a driver'soutboard ear as a function of engine revolutions per minute (rpm), andwhere the interior loudness is measured in Sones, there is in turncalculated in a first instance from the measured interior loudnesses asa function of engine revolutions per minuted (rpm) in Sones a series ofinterior loudnesses in Phons as a function of engine revolutions perminute (rpm), while employing equation 1, as follows.

P=40+10 log₂(S _(t))  (1)

Within equation 1, S_(t) represents an individual measured interiorloudness data point in Sones and P represents an individual calculatedloudness data point in Phons calculated from the individual measuredinterior loudness data point in Sones. Similarly, within the preferredembodiment of the present invention, there is also then calculated fromthe measured interior loudness data points in Sones as a function ofengine revolutions per minute (rpm) a slope of the least squares bestfit linear regression line through the measured interior loudness datapoints in Sones as a function of engine revolutions per minute (rpm)curve. This variable is defined as sm. Yet similarly, there is then alsofurther calculated from the calculated interior loudness data points inPhons as a function of engine revolutions per minute (rpm) a peak Phonsvalue over the entire Phons versus engine revolutions per minute (rpm)curve. This variable is defined as pmax. Finally yet similarly, there isthen also further calculated from the calculated interior loudness datapoints in Phons as a function of engine revolutions per minute (rpm) aPhons weighted average defined as the area under the Phons versus enginerevolutions per minute (rpm) curve over the entire range of the Phonsversus engine revolutions per minute (rpm) curve. This final variable isdefined as pwavg.

Referring again to FIG. 1, there is illustrated in conjunction withreference numeral 50 the next process step in accord with the method ofthe present invention. In accord with the process step which correspondswith reference numeral 50, there is then correlated the series ofmeasured subjective customer satisfaction levels with the series of NVHvariable levels to provide a correlation. Alternatively, within thepresent invention there may under certain circumstances be correlatedthe series of NVH levels rather than the series of NVH variable levels,to provide the correlation, where the NVH levels and the NVH variablelevels are, within the context of the present invention, further definedas NVH parameters. Within the context of the preferred embodiment of thepresent invention, the correlation of the measured customer satisfactionlevels with the series of NVH variable levels is typically andpreferably undertaken by providing a plot of the series of measuredcustomer satisfaction levels as a function of each of the threeforegoing calculated NVH variable levels (i.e., sm, pmax and pwavg).

An example of such a plot is provided in FIG. 2 wherein there isillustrated a plot of Measured High Customer Satisfaction (in percent)(i.e., percentage of customers reporting a 9 or a 10 on a 1-10 scale)versus Phons Weighted Average of Phons versus Revolutions Per Minute (inPhons), for a series of eleven four wheel drive sport utility vehicleswhich is not otherwise specifically identified, but which represents aselection of four wheel drive sport utility vehicles which may beobtained from a plurality of transportation vehicle manufacturers.Within the context specifically of FIG. 2, each of the four wheel drivesport utility vehicles points is defined by a sample of singleidentified vehicle owners, where each owned their vehicle for a timeperiod of from about 2.5 to about 3.5 months during which time periodthe individual four wheel drive sport utility vehicles had accrued anodometer mileage of from about 3000 to about 7000 miles.

As is also shown within the plot of FIG. 2, there is determined a curvewhich correlates the data points directed towards Measured High CustomerSatisfaction Level versus Phons Weighted Average of Phons versus EngineRevolutions Per Minute. With respect to the curve which correlates thedata points, it has been determined experientially within the context ofthe present invention that an optimal correlation of the data points isobtained within a plot of customer satisfaction versus a NVH variable ora NVH parameter, such as the graph as is illustrated within the FIG. 2,while employing a Transformed Gamma Distribution (TGD) model withrespect to each measured or calculated variable which is correlated withsubjective customer satisfaction level. The Transformed GammaDistribution (TGD) is defined in equation 2, as follows. $\begin{matrix}{{{TGD}\left( {x,\alpha,\beta} \right)} = \left\{ \begin{matrix}{{{\left\lbrack {1 - {{CGD}\left( {{x - {Xshift}},\alpha,\beta} \right)}} \right\rbrack*\left( {{100\%} - {CSmin}} \right)} + {CSmin}};} & {x > {Xshift}} \\{{100\%};} & {otherwise}\end{matrix} \right.} & (2)\end{matrix}$

where $\begin{matrix}{{{CGD}\left( {x,\alpha,\beta} \right)} = {\int_{0}^{x}\left( {\frac{1}{\beta^{\alpha}{\Gamma (\alpha)}}x^{\alpha - 1}e^{- \frac{x}{\beta}}} \right)}} & (3)\end{matrix}$

Within equation 2, x represents a particular NVH variable or NVHparameter. CSmin represents the minimum Customer Satisfaction leveldefined by the lower vertical asymptote as seen in FIG. 2. Xshiftrepresents the value of a particular NVH variable or NVH parameter whenthe Measured High Customer Satisfaction Level reaches 100%. α and βrepresent statistical coefficients that affect the shape of the curveshown in FIG. 2. α and β are coefficients from the Cumulative GammaDistribution (CGD), which is shown in equation 3. The Cumulative GammaDistribution (CGD) is a standard statistical curve and is defined within“Probability and Statistics for Engineering and the Sciences” (Jay L.Devore, Brooks/Cole Publishing Company, 1991, pp. 157-159)

Similarly, the Transformed Gamma Distribution (TGD) model employs aunique intelligent range reduction scheme while employing a fit definedas the Least Combination Error (LCE) fit which provides optimal curvefitting (i.e., determination of curve fitting coefficients), rather thana Least Square Error (LSE) fit, which is generally more common in theart of statistical analysis. The Least Combination Error (LCE) fit is acombination of the standard Least Square Error (LSE) fit and the LeastTrimmed Squares Error (LTSE) fit. In discussing curve fits it isnecessary to define the Residual (r_(i)) in equation 4, as follows:

r _(i) =y _(i) −ŷ _(i)  (4)

Within equation 4, y_(i) represents the actual or measured value andy_(i) represents the predicted or estimated value of y_(i) from thecorrelation. LSE and LTSE fit are clearly defined within “RobustRegression and Outlier Detection” (Peter J. Rousseeuw and Annick M.Leroy, John Wiley & Sons Inc., 1987, pp. 1-3, 14-15, 132-135) and asfollows. $\begin{matrix}{{{Minimize}\quad {{LSE}(n)}} \equiv {\sum\limits_{i = 1}^{n}r_{i}^{2}}} & (5) \\{{{{Minimize}\quad {{LTSE}\left( {n,h} \right)}} \equiv {\sum\limits_{i = 1}^{h}\left( r^{2} \right)_{nn}}},{{{where}\quad \left( r^{2} \right)_{1n}} \leq \left( r^{2} \right)_{2n} \leq \hat{E} \leq \left( r^{2} \right)_{nn}}} & (6)\end{matrix}$

where

h(trim,p,n)=Int([n(1−trim)]+[trim(p+1)])  (7)

Within equation 5, n represents the sample size or the total number ofvehicles used for correlation. Within equation 6, each (r²)_(i:n)represents a squared residual, ordered from smallest to largest, hrepresents an integer between 1 and n, defined by equation 7. Withinequation 7, trim represents the trimming percentage to be used, wheretrim is from 0% to 50%, p represents the number of curve coefficientsrequired for the curve, which always equals 4 for the TGD. Using thesedefinitions, the Least Combination Error (LCE) fit Minimizes LCE whereLCE is defined as follows.

LCE=[(LSEwgt*(LSE(n)/n)+LTSE1wgt*(LTSE(n,h ₁)/h ₁)+LTSE2wgt*(LTSE(n,h₂)/h ₂))/LCEtotwgt]*n  (8)

where

h ₁ =h(trim/2,p,n);h ₂ =h(trim,p,n)  (9),(10)

LCEtotwgt=LSEwgt+LTSE1wgt+LTSE2wgt  (11)

Within equation 8, LSEwgt, LTSE1wgt and LTSE2wgt are the weightingfactors applied to the three combined fits included within the LCE fit.

The intelligent range reduction scheme used when employing the abovementioned Least Combination Error (LCE) fit is both unique andnecessary. The four curve coefficients of the Transformed GammaDistribution (TGD) model have the following properties and rangerestrictions. The range of possible CSmin values is between 0% and 100%of High Customer Satisfaction. The range of possible Xshift values isbetween zero and positive infinity. The range of α and β is typicallyand preferably restricted to 0.1 to 50.1 for both. Finding thecoefficient values to acheive the minimum value of LCE is by definitiona Multivariable Constrained Optimization. Because of the nonlinearitiesof the TGD model, it can be shown that across the possible coefficientranges the optimal solution (ie. at the global minimum of LCE) lies in aSaddle Region at a Saddle Point, described within “Vector Calculus”(3^(rd) Edition, Jerrold E. Marsden and Anthony J. Tromba, W. H. Freemanand Company, 1988, pp. 248-256). It can also be shown that across thepossible coefficient ranges the TGD model is not unimodal or has morethan one local minimum. As such, the typical search methods and gradientassisted search as described within “Design and Optimization of ThermalSystems” (Yogesh Jaluria, McGraw-Hill, 1998, pp. 448-484) do not work orso computational demanding that a single correlation could take days tomonths to complete.

The intelligent range reduction scheme employed solves these problems inthe following manner. The ranges of the data points, made up of theindividual vehicles, are scanned. Based on these input ranges theinitial ranges or initial ranges of uncertainty are set as follows.CSmin has its initial range set to an interval around the minimumCustomer Satisfaction value in the data points, with this minimum as thecenter. Xshift has its initial range set to an interval around minimumvalue of NVH variable or NVH parameter in the data points, with thisminimum as the center. The initial ranges both α and β are typically andpreferably set to 0.1 to 50.1 for both. Then a course grid is setupacross CSmin, Xshift and one of α and β. In the first iteration of thesearch, all the points defined by the grids or subdivisions of CSmin,Xshift and one of α and β (ie. CSmin, Xshift and β) define subranges orsubdomains of uncertainty. Each such subrange is then searched along theother of α and β, which was not subdivided into a grid (ie. α) by usinga Fibonacci Search, which as defined in the last reference, and using aspecified tolerance for the value of the other of α and β (ie. α). Atthe completion of the first iteration the subranges or subdomains thatmay contain the optimum solution or global minimum of LCE areidentified. In the next iteration each identified subrange or subdomainis searched in a similar manner to the first iteration, with regionsaround each subrange or subdomain that is larger than the identifiedsubrange or subdomain. In each successive iteration the ranges of thecoefficients that is seached within each subdoman is reduced, except forthe coefficient of α and β that is searched using a Fibonacci Search,whose searching range or interval of uncertainty is fixed at 0.1 to 50.1for all iterations and subdomains. This pattern of iterations iscontinued until the defined tolerances on each coefficient are reachedand the optimum solution with the global minimum LCE is identified.Essentially the intelligent range reduction scheme surveys the entireinitial region of uncertainty and then zooms in telescopically on eachidentified subregion until the optimum solution is found. Thisintelligent range reduction scheme reduces computing time by over 20times versus an equivalent Exhaustive search method, which is defined inthe last reference.

Referring again to FIG. 1, there is illustrated in conjunction withreference numeral 60 a final step in accord with the method of thepresent invention. In accord with the process step which correspondswith reference numeral 60, there is then extrapolated or interpolatedfrom the correlation of measured subjective customer satisfaction levelswith the series of NVH variable levels for an additional transportationvehicle to provide an expected subjective customer satisfaction levelfor the additional transportation vehicle for which there does not exista measured subjective customer satisfaction level but for which theredoes exist a NVH variable level, either measured or derived from acomputer model or simulation.

Within the context of the preferred embodiment of the present invention,the transportation vehicle for which there does not exist a measuredsubjective customer satisfaction level will typically and preferably bea new transportation vehicle or a prototype transportation vehicle.Under such circumstances, and in particular for a prototypetransportation vehicle, an expected subjective customer satisfactionlevel early in its design, development and manufacturing is desirablesuch that design, development and manufacturing revisions may be made toprovide the transportation vehicle with optimized customer satisfactionlevel.

While the invention as disclosed within the schematic process flowdiagram of FIG. 1 may in itself be employed for determining by means ofextrapolation or interpolation an expected subjective customersatisfaction level for a transportation vehicle based upon correlationwith a NVH variable level derived from a NVH level, the presentinvention also provides that there may be determined within the contextof the present invention customer satisfaction for a transportationvehicle based upon a correlation which employs multiple measured orcalculated variables, such as NVH variables, which in an aggregate arecorrelated with measured subjective customer satisfaction levels, andwherein there may subsequently interpolated or extrapolated from anaggregate of the correlated measurements an expected subjective customersatisfaction for an additional transportation vehicle for which theredoes not exist a measured subjective customer satisfaction level, butfor which there does exist a NVH variable level.

Within the context of the preferred embodiment of the present inventionwhere there is calculated from interior loudness measurements at adriver's outboard ear: (1) the slope of a Sones versus enginerevolutions per minute (rpm) curve (i.e., sm); (2) the peak of a Phonsover a Phons versus engine revolutions per minute (rpm) curve (i.e.,pmax); and (3) the weighted average of a Phons versus engine revolutionsper minute (rpm) curve (i.e., pwavg), there is first determined aTransformed Gamma Distribution (TGD) correlation, as is described in thepreviously described process step with reference numeral 50 (FIG. 1),for each NVH parameter which are typically and preferably loudnessparameters (ie. sm, pmax, pwavg).

With respect to the preferred embodiment of the present invention,wherein there is employed for determining a Transformed GammaDistribution (TGD) any of the three variables sm, pmax and pwavg, theremay then be combined the three Transformed Gamma Distribution (TGD)functions as determined in accord with equation 2 in a fashion whichequates measured customer satisfaction levels with an aggregate of thethree calculated variables sm, pmax and pwavg, further in accord withequation 12, as follows.

CS=F(sm,pmax,pwavg)=a*TGD(sm,α,β)+b*TGD(pmax,α,β)+c*TGD(pwavg,α,β)  (12)

Within equation 12: (1) CS is customer satisfaction, typically in termsof high customer satisfaction (percent); and (2) the coefficients a, band c are determined experimentally while employing the same uniqueintelligent range reduction scheme as is employed for determining thecoefficients and within equation 2. The initial range for thecoefficients a, b and c must be between −1 and 1 and is typically andpreferably between 0 and 1 for the loudness variables (ie. sm, pmax,pwavg). As is illustrated within the plot of FIG. 3, which is directedtowards an aggregate of predicted versus measured high customersatisfaction levels predicated upon the loudness variables sm, pmax andpwavg, for the same series of eleven wheel drive sport utilitytransportation vehicles as employed within FIG. 2, there is observed aunity of correlation between predicted and measured high subjectivecustomer satisfaction levels, suggesting that the mathematic assumptionsand model upon which the present invention is predicated provides anoperative invention.

As is understood by a person skilled in the art, the preferredembodiment and examples of the present invention are illustrative of thepresent invention rather than limiting of the present invention.Revisions and modifications may be made to transportation vehicle types,measured parameters and calculated variables through which is practicedthe method of the present invention, while still providing a method inaccord with the present invention, further in accord with theaccompanying claims.

What is claimed is:
 1. A method for predicting customer satisfaction for a transportation vehicle comprising: providing a group of transportation vehicles; measuring for the group of transportation vehicles a series of subjective customer satisfaction levels; measuring for the group of transportation vehicles a series of Noise, Vibration and Harshness (NVH) levels; correlating, while employing a Least Combination Error (LCE) fit, the series of subjective customer satisfaction levels with the series of Noise, Vibration and Harshness (NVH) levels to provide a correlation; and employing the correlation to provide an expected subjective customer satisfaction level for an additional transportation vehicle for which there does not exist an additional measured subjective customer satisfaction level, but for which there does exist a Noise, Vibration and Harshness (NVH) variable level.
 2. The method of claim 1 wherein the transportation vehicle is selected from the group consisting of passenger transportation vehicles, sport utility transportation vehicles, light utility transportation vehicles and heavy utility transportation vehicles.
 3. The method or claim 1 wherein the Noise, Vibration and Harshness (NVH) level is selected from the group consisting of a vibration level and noise level.
 4. The method of claim 3 wherein the vibration level is selected from the group consisting of a steering wheel vibration level, a seat track vibration level, shift stick vibration level, and a frame/chassis vibration level.
 5. The method of claim 3 wherein the noise level is selected from the group consisting of an interior noise loudness, an engine noise loudness, a powertrain noise loudness, an intake system noise loudness and an exhaust system noise loudness.
 6. The method of claim 5 wherein the interior noise loudness is measured at a location of a driver's outboard ear within an interior of the transportation vehicle.
 7. A method for predicting customer satisfaction for a transportation vehicle comprising: providing a group of transportation vehicles; measuring for the group of transportation vehicles a series of subjective customer satisfaction levels; measuring for the group of transportation vehicles a series of Noise, Vibration and Harshness (NVH) levels within the context of a series of a interior loudness parameters, wherein the series of interior loudness parameters is determined employing each of: a slope of a linear regression line through a Phons versus an engine revolution per minute (rpm) curve for the group or transportation vehicles; a maximum Phons over an entire range for a Phons versus the engine revolution per minute (rpm) curve for the group of transportation vehicles; and a Phons weighted average over an entire range for a Phons versus the engine revolution per minute (rpm) curve for the group of transportation vehicles; correlating the series of subjective customer satisfaction levels with the series of Noise, Vibration and Harshness (NVH) levels to provide a correlation; and employing the correlation to provide and expected subjective customer satisfaction level for an additional transportation vehicle for which there does not exist an additional measured subjective customer satisfaction level, but for which there exist a Noise, Vibration and Harshness (NVH) variable level.
 8. The method of claim 7 wherein the group of transportation vehicles is selected from the group consisting of passenger transportation vehicles, sport utility transportation vehicles, light utility transportation of vehicles and heavy utility transportation vehicles.
 9. The method of claim 7 wherein the series of Noise, Vibration and Harshness (NVH) levels is selected from the group consisting of a series of vibration levels and a series of noise levels.
 10. The method of claim 9 wherein the series of vibration levels is selected from the group consisting of steering wheel vibration levels, seat track vibration levels, shift stick vibration levels, and frame/chassis vibration levels.
 11. The method of claim 9 wherein the series of noise level is selected from the group consisting of interior noise loudness levels, engine noise loudness levels, powertrain noise loudness levels, intake system noise loudness levels and exhaust system noise loudness levels.
 12. The method of claim 11 wherein the series of interior noise loudness levels is measured at a location of a driver's outboard ear within a series of interiors of the group of transportation vehicles. 